Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of cellular composition within complex tissues and organisms. A major limitation in most scRNAseq analysis pipelines is the reliance on manual annotations to determine cell identities, which are time consuming, subjective, and require expertise. Given the surge in cell sequencing, supervised methods-especially deep learning models-have been developed for automatic cell type identification (ACTI), which achieve high accuracy and scalability. However, all existing deep learning frameworks for ACTI lack interpretability and are used as "black-box" models. We present N-ACT (Neural-Attention for Cell Type identification): the first-of-its-kind interpretable deep neural network for ACTI utilizing neural-attention to detect salient genes for use in cell-type identification. We compare N-ACT to conventional annotation methods on two previously manually annotated data sets, demonstrating that N-ACT accurately identifies marker genes and cell types in an unsupervised manner, while performing comparably on multiple data sets to current state-of-the-art model in traditional supervised ACTI.

N-ACT是第一个可解释的深度神经网络，利用神经-注意力检测显著基因，用于细胞类型识别的自动细胞类型识别，通过将其与传统的监督式细胞类型识别方法进行比较，证明N-ACT在无监督的情况下准确识别标记基因和细胞类型，同时在多个数据集上表现与传统监督的ACTI现有技术水平相当。

N-ACT：一种可解释的深度学习模型用于自动细胞类型和显著基因的识别